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arXiv · 2011.06709

Active Reinforcement Learning: Observing Rewards at a Cost

David Krueger·Jan Leike·Owain Evans·John Salvatier
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Abstract

Active reinforcement learning (ARL) is a variant on reinforcement learning where the agent does not observe the reward unless it chooses to pay a query cost c > 0. The central question of ARL is how to quantify the long-term value of reward information. Even in multi-armed bandits, computing the value of this information is intractable and we have to rely on heuristics. We propose and evaluate several heuristic approaches for ARL in multi-armed bandits and (tabular) Markov decision processes, and discuss and illustrate some challenging aspects of the ARL problem.

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David Krueger, Jan Leike, Owain Evans, John Salvatier. 2020-11-13. Active Reinforcement Learning: Observing Rewards at a Cost. https://arxiv.org/abs/2011.06709

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